Overview

Dataset statistics

Number of variables12
Number of observations61
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 KiB
Average record size in memory98.1 B

Variable types

Numeric11
Categorical1

Alerts

Country Name has constant value "India" Constant
Year is highly correlated with GDP (current US$) and 7 other fieldsHigh correlation
GDP (current US$) is highly correlated with Year and 7 other fieldsHigh correlation
GDP per capita (current US$) is highly correlated with Year and 7 other fieldsHigh correlation
GDP growth (annual %) is highly correlated with Country NameHigh correlation
Imports of goods and services (% of GDP) is highly correlated with Year and 7 other fieldsHigh correlation
Exports of goods and services (% of GDP) is highly correlated with Year and 7 other fieldsHigh correlation
Total reserves (includes gold, current US$) is highly correlated with Year and 7 other fieldsHigh correlation
Inflation, consumer prices (annual %) is highly correlated with Life expectancy at birth, total (years)High correlation
Population, total is highly correlated with Year and 7 other fieldsHigh correlation
Population growth (annual %) is highly correlated with Year and 7 other fieldsHigh correlation
Life expectancy at birth, total (years) is highly correlated with Year and 8 other fieldsHigh correlation
Country Name is highly correlated with Year and 10 other fieldsHigh correlation
Year is uniformly distributed Uniform
Year has unique values Unique
GDP (current US$) has unique values Unique
Imports of goods and services (% of GDP) has unique values Unique
Total reserves (includes gold, current US$) has unique values Unique
Inflation, consumer prices (annual %) has unique values Unique
Population, total has unique values Unique
Life expectancy at birth, total (years) has unique values Unique
GDP growth (annual %) has 1 (1.6%) zeros Zeros

Reproduction

Analysis started2022-11-06 10:03:59.670683
Analysis finished2022-11-06 10:04:14.789174
Duration15.12 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Year
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1990
Minimum1960
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size616.0 B
2022-11-06T15:34:14.864868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1960
5-th percentile1963
Q11975
median1990
Q32005
95-th percentile2017
Maximum2020
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.75293403
Coefficient of variation (CV)0.008921072377
Kurtosis-1.2
Mean1990
Median Absolute Deviation (MAD)15
Skewness0
Sum121390
Variance315.1666667
MonotonicityStrictly increasing
2022-11-06T15:34:15.062004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19601
 
1.6%
19911
 
1.6%
19931
 
1.6%
19941
 
1.6%
19951
 
1.6%
19961
 
1.6%
19971
 
1.6%
19981
 
1.6%
19991
 
1.6%
20001
 
1.6%
Other values (51)51
83.6%
ValueCountFrequency (%)
19601
1.6%
19611
1.6%
19621
1.6%
19631
1.6%
19641
1.6%
19651
1.6%
19661
1.6%
19671
1.6%
19681
1.6%
19691
1.6%
ValueCountFrequency (%)
20201
1.6%
20191
1.6%
20181
1.6%
20171
1.6%
20161
1.6%
20151
1.6%
20141
1.6%
20131
1.6%
20121
1.6%
20111
1.6%

Country Name
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size616.0 B
India
61 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters305
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndia
2nd rowIndia
3rd rowIndia
4th rowIndia
5th rowIndia

Common Values

ValueCountFrequency (%)
India61
100.0%

Length

2022-11-06T15:34:15.162241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-06T15:34:15.247417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
india61
100.0%

Most occurring characters

ValueCountFrequency (%)
I61
20.0%
n61
20.0%
d61
20.0%
i61
20.0%
a61
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter244
80.0%
Uppercase Letter61
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n61
25.0%
d61
25.0%
i61
25.0%
a61
25.0%
Uppercase Letter
ValueCountFrequency (%)
I61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin305
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I61
20.0%
n61
20.0%
d61
20.0%
i61
20.0%
a61
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII305
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I61
20.0%
n61
20.0%
d61
20.0%
i61
20.0%
a61
20.0%

GDP (current US$)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.58472482 × 1011
Minimum3.702988388 × 1010
Maximum2.831552223 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size616.0 B
2022-11-06T15:34:15.311996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.702988388 × 1010
5-th percentile4.586546203 × 1010
Q19.952589912 × 1010
median2.882084304 × 1011
Q38.203815955 × 1011
95-th percentile2.651472946 × 1012
Maximum2.831552223 × 1012
Range2.794522339 × 1012
Interquartile range (IQR)7.208556964 × 1011

Descriptive statistics

Standard deviation8.129605664 × 1011
Coefficient of variation (CV)1.234615855
Kurtosis0.9374817574
Mean6.58472482 × 1011
Median Absolute Deviation (MAD)2.208574424 × 1011
Skewness1.487063725
Sum4.01668214 × 1013
Variance6.609048825 × 1023
MonotonicityNot monotonic
2022-11-06T15:34:15.425813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.702988388 × 10101
 
1.6%
2.701053419 × 10111
 
1.6%
2.79296023 × 10111
 
1.6%
3.272755835 × 10111
 
1.6%
3.602819527 × 10111
 
1.6%
3.928970543 × 10111
 
1.6%
4.158677539 × 10111
 
1.6%
4.213514775 × 10111
 
1.6%
4.588204173 × 10111
 
1.6%
4.683949373 × 10111
 
1.6%
Other values (51)51
83.6%
ValueCountFrequency (%)
3.702988388 × 10101
1.6%
3.923243578 × 10101
1.6%
4.216148186 × 10101
1.6%
4.586546203 × 10101
1.6%
4.842192346 × 10101
1.6%
5.01349422 × 10101
1.6%
5.308545587 × 10101
1.6%
5.648028994 × 10101
1.6%
5.844799502 × 10101
1.6%
5.955485458 × 10101
1.6%
ValueCountFrequency (%)
2.831552223 × 10121
1.6%
2.702929719 × 10121
1.6%
2.667687952 × 10121
1.6%
2.651472946 × 10121
1.6%
2.294797981 × 10121
1.6%
2.103587814 × 10121
1.6%
2.039127446 × 10121
1.6%
1.856722121 × 10121
1.6%
1.827637859 × 10121
1.6%
1.823049928 × 10121
1.6%

GDP per capita (current US$)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct58
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean575.557377
Minimum82
Maximum2101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size616.0 B
2022-11-06T15:34:15.527210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile90
Q1161
median340
Q3715
95-th percentile1928
Maximum2101
Range2019
Interquartile range (IQR)554

Descriptive statistics

Standard deviation584.0790621
Coefficient of variation (CV)1.01480597
Kurtosis0.6211983977
Mean575.557377
Median Absolute Deviation (MAD)207
Skewness1.382711036
Sum35109
Variance341148.3508
MonotonicityNot monotonic
2022-11-06T15:34:15.626448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
902
 
3.3%
3462
 
3.3%
1192
 
3.3%
821
 
1.6%
10281
 
1.6%
4001
 
1.6%
4151
 
1.6%
4131
 
1.6%
4421
 
1.6%
4431
 
1.6%
Other values (48)48
78.7%
ValueCountFrequency (%)
821
1.6%
851
1.6%
902
3.3%
961
1.6%
1001
1.6%
1011
1.6%
1081
1.6%
1121
1.6%
1161
1.6%
1192
3.3%
ValueCountFrequency (%)
21011
1.6%
19971
1.6%
19811
1.6%
19281
1.6%
17331
1.6%
16061
1.6%
15741
1.6%
14581
1.6%
14501
1.6%
14441
1.6%

GDP growth (annual %)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct59
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.938196721
Minimum-7.25
Maximum9.63
Zeros1
Zeros (%)1.6%
Negative5
Negative (%)8.2%
Memory size616.0 B
2022-11-06T15:34:15.748823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-7.25
5-th percentile-0.55
Q13.72
median5.53
Q37.45
95-th percentile8.5
Maximum9.63
Range16.88
Interquartile range (IQR)3.73

Descriptive statistics

Standard deviation3.344890884
Coefficient of variation (CV)0.6773506754
Kurtosis2.899149891
Mean4.938196721
Median Absolute Deviation (MAD)1.92
Skewness-1.507247932
Sum301.23
Variance11.18829503
MonotonicityNot monotonic
2022-11-06T15:34:15.848814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.922
 
3.3%
7.862
 
3.3%
01
 
1.6%
5.481
 
1.6%
4.751
 
1.6%
6.661
 
1.6%
7.571
 
1.6%
7.551
 
1.6%
4.051
 
1.6%
6.181
 
1.6%
Other values (49)49
80.3%
ValueCountFrequency (%)
-7.251
1.6%
-5.241
1.6%
-2.641
1.6%
-0.551
1.6%
-0.061
1.6%
01
1.6%
1.061
1.6%
1.191
1.6%
1.641
1.6%
1.661
1.6%
ValueCountFrequency (%)
9.631
1.6%
9.151
1.6%
8.851
1.6%
8.51
1.6%
8.261
1.6%
8.061
1.6%
81
1.6%
7.922
3.3%
7.862
3.3%
7.831
1.6%

Imports of goods and services (% of GDP)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.74639344
Minimum3.71
Maximum31.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size616.0 B
2022-11-06T15:34:15.964482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.71
5-th percentile4.03
Q16.59
median8.57
Q319.64
95-th percentile28.41
Maximum31.26
Range27.55
Interquartile range (IQR)13.05

Descriptive statistics

Standard deviation8.155109857
Coefficient of variation (CV)0.6397974371
Kurtosis-0.5647548495
Mean12.74639344
Median Absolute Deviation (MAD)3.36
Skewness0.894043563
Sum777.53
Variance66.50581678
MonotonicityNot monotonic
2022-11-06T15:34:16.049457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.831
 
1.6%
8.491
 
1.6%
9.821
 
1.6%
10.191
 
1.6%
12.021
 
1.6%
11.541
 
1.6%
11.931
 
1.6%
12.681
 
1.6%
13.361
 
1.6%
13.91
 
1.6%
Other values (51)51
83.6%
ValueCountFrequency (%)
3.711
1.6%
3.881
1.6%
41
1.6%
4.031
1.6%
4.721
1.6%
4.941
1.6%
5.211
1.6%
5.691
1.6%
5.911
1.6%
5.951
1.6%
ValueCountFrequency (%)
31.261
1.6%
31.081
1.6%
29.271
1.6%
28.411
1.6%
26.851
1.6%
25.951
1.6%
25.871
1.6%
24.891
1.6%
24.461
1.6%
23.691
1.6%

Exports of goods and services (% of GDP)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct60
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.88557377
Minimum3.31
Maximum25.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size616.0 B
2022-11-06T15:34:16.165315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.31
5-th percentile3.73
Q15.2
median7.05
Q318.69
95-th percentile24.1
Maximum25.43
Range22.12
Interquartile range (IQR)13.49

Descriptive statistics

Standard deviation7.060457616
Coefficient of variation (CV)0.648606841
Kurtosis-0.9845493807
Mean10.88557377
Median Absolute Deviation (MAD)3.27
Skewness0.7175709051
Sum664.02
Variance49.85006175
MonotonicityNot monotonic
2022-11-06T15:34:16.265555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.032
 
3.3%
4.461
 
1.6%
21.271
 
1.6%
9.891
 
1.6%
10.841
 
1.6%
10.391
 
1.6%
10.691
 
1.6%
11.021
 
1.6%
11.451
 
1.6%
131
 
1.6%
Other values (50)50
82.0%
ValueCountFrequency (%)
3.311
1.6%
3.671
1.6%
3.711
1.6%
3.731
1.6%
3.781
1.6%
4.032
3.3%
4.041
1.6%
4.141
1.6%
4.171
1.6%
4.211
1.6%
ValueCountFrequency (%)
25.431
1.6%
24.541
1.6%
24.531
1.6%
24.11
1.6%
22.971
1.6%
22.41
1.6%
21.271
1.6%
20.81
1.6%
20.41
1.6%
19.931
1.6%

Total reserves (includes gold, current US$)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.802227322 × 1010
Minimum499145125.8
Maximum5.902273599 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size616.0 B
2022-11-06T15:34:16.365858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum499145125.8
5-th percentile607862500.4
Q12324650347
median1.15117406 × 1010
Q31.378248283 × 1011
95-th percentile3.991671592 × 1011
Maximum5.902273599 × 1011
Range5.897282148 × 1011
Interquartile range (IQR)1.355001779 × 1011

Descriptive statistics

Standard deviation1.49710187 × 1011
Coefficient of variation (CV)1.527307847
Kurtosis1.189565938
Mean9.802227322 × 1010
Median Absolute Deviation (MAD)1.09038781 × 1010
Skewness1.504361246
Sum5.979358667 × 1012
Variance2.24131401 × 1022
MonotonicityNot monotonic
2022-11-06T15:34:16.478479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
674536630.91
 
1.6%
76159874751
 
1.6%
1.467462709 × 10101
 
1.6%
2.422092847 × 10101
 
1.6%
2.286463692 × 10101
 
1.6%
2.488936466 × 10101
 
1.6%
2.838537295 × 10101
 
1.6%
3.064656366 × 10101
 
1.6%
3.600529593 × 10101
 
1.6%
4.105906157 × 10101
 
1.6%
Other values (51)51
83.6%
ValueCountFrequency (%)
499145125.81
1.6%
5127918441
1.6%
600850886.21
1.6%
607862500.41
1.6%
609694584.51
1.6%
663764119.81
1.6%
666357094.91
1.6%
674536630.91
1.6%
730352744.91
1.6%
927764119.81
1.6%
ValueCountFrequency (%)
5.902273599 × 10111
1.6%
4.634699022 × 10111
1.6%
4.12613792 × 10111
1.6%
3.991671592 × 10111
1.6%
3.61694322 × 10111
1.6%
3.53319061 × 10111
1.6%
3.250810351 × 10111
1.6%
3.004801688 × 10111
1.6%
3.004255174 × 10111
1.6%
2.987394631 × 10111
1.6%

Inflation, consumer prices (annual %)
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.413278689
Minimum-7.63
Maximum28.6
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)3.3%
Memory size616.0 B
2022-11-06T15:34:16.665859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-7.63
5-th percentile1.78
Q14.01
median6.67
Q310.02
95-th percentile13.36
Maximum28.6
Range36.23
Interquartile range (IQR)6.01

Descriptive statistics

Standard deviation4.940152738
Coefficient of variation (CV)0.6663924217
Kurtosis5.464469638
Mean7.413278689
Median Absolute Deviation (MAD)2.81
Skewness0.9644994689
Sum452.21
Variance24.40510907
MonotonicityNot monotonic
2022-11-06T15:34:16.766094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.781
 
1.6%
13.871
 
1.6%
6.331
 
1.6%
10.251
 
1.6%
10.221
 
1.6%
8.981
 
1.6%
7.161
 
1.6%
13.231
 
1.6%
4.671
 
1.6%
4.011
 
1.6%
Other values (51)51
83.6%
ValueCountFrequency (%)
-7.631
1.6%
-0.581
1.6%
1.71
1.6%
1.781
1.6%
2.521
1.6%
2.951
1.6%
3.081
1.6%
3.241
1.6%
3.331
1.6%
3.631
1.6%
ValueCountFrequency (%)
28.61
1.6%
16.941
1.6%
13.871
1.6%
13.361
1.6%
13.231
1.6%
13.111
1.6%
13.061
1.6%
11.991
1.6%
11.871
1.6%
11.791
1.6%

Population, total
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean891394550.1
Minimum445954579
Maximum1396387127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size616.0 B
2022-11-06T15:34:16.885585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum445954579
5-th percentile477933619
Q1623524219
median870452165
Q31154638713
95-th percentile1354195680
Maximum1396387127
Range950432548
Interquartile range (IQR)531114494

Descriptive statistics

Standard deviation297449648.7
Coefficient of variation (CV)0.3336902257
Kurtosis-1.315476685
Mean891394550.1
Median Absolute Deviation (MAD)265812418
Skewness0.1440092234
Sum5.437506756 × 1010
Variance8.847629349 × 1016
MonotonicityStrictly increasing
2022-11-06T15:34:17.000171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4459545791
 
1.6%
8889417561
 
1.6%
9263512971
 
1.6%
9452619581
 
1.6%
9642791291
 
1.6%
9832812181
 
1.6%
10023352301
 
1.6%
10214345761
 
1.6%
10405000541
 
1.6%
10596336751
 
1.6%
Other values (51)51
83.6%
ValueCountFrequency (%)
4459545791
1.6%
4563518761
1.6%
4670241931
1.6%
4779336191
1.6%
4890593091
1.6%
5001143461
1.6%
5109926171
1.6%
5219870691
1.6%
5334319091
1.6%
5453146701
1.6%
ValueCountFrequency (%)
13963871271
1.6%
13831120501
1.6%
13690033061
1.6%
13541956801
1.6%
13386363401
1.6%
13228665051
1.6%
13072465091
1.6%
12911320631
1.6%
12744872151
1.6%
12576211911
1.6%

Population growth (annual %)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct45
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.927704918
Minimum0.96
Maximum2.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size616.0 B
2022-11-06T15:34:17.098402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.96
5-th percentile1.16
Q11.62
median2.15
Q32.26
95-th percentile2.33
Maximum2.34
Range1.38
Interquartile range (IQR)0.64

Descriptive statistics

Standard deviation0.4190242373
Coefficient of variation (CV)0.2173694913
Kurtosis-0.6221500313
Mean1.927704918
Median Absolute Deviation (MAD)0.15
Skewness-0.8988850476
Sum117.59
Variance0.1755813115
MonotonicityNot monotonic
2022-11-06T15:34:17.198637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
2.234
 
6.6%
2.243
 
4.9%
2.263
 
4.9%
2.293
 
4.9%
2.283
 
4.9%
2.332
 
3.3%
2.252
 
3.3%
1.192
 
3.3%
2.192
 
3.3%
2.342
 
3.3%
Other values (35)35
57.4%
ValueCountFrequency (%)
0.961
1.6%
1.031
1.6%
1.091
1.6%
1.161
1.6%
1.192
3.3%
1.251
1.6%
1.311
1.6%
1.341
1.6%
1.371
1.6%
1.391
1.6%
ValueCountFrequency (%)
2.342
3.3%
2.332
3.3%
2.311
 
1.6%
2.31
 
1.6%
2.293
4.9%
2.283
4.9%
2.271
 
1.6%
2.263
4.9%
2.252
3.3%
2.243
4.9%

Life expectancy at birth, total (years)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.14622951
Minimum41.13
Maximum69.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size616.0 B
2022-11-06T15:34:17.298871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum41.13
5-th percentile42.94
Q150.63
median57.66
Q364.31
95-th percentile68.97
Maximum69.73
Range28.6
Interquartile range (IQR)13.68

Descriptive statistics

Standard deviation8.459559122
Coefficient of variation (CV)0.1480335482
Kurtosis-1.073089409
Mean57.14622951
Median Absolute Deviation (MAD)7.03
Skewness-0.2553983687
Sum3485.92
Variance71.56414055
MonotonicityStrictly increasing
2022-11-06T15:34:17.410354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.131
 
1.6%
58.151
 
1.6%
59.121
 
1.6%
59.591
 
1.6%
60.061
 
1.6%
60.531
 
1.6%
611
 
1.6%
61.471
 
1.6%
61.881
 
1.6%
62.281
 
1.6%
Other values (51)51
83.6%
ValueCountFrequency (%)
41.131
1.6%
41.741
1.6%
42.341
1.6%
42.941
1.6%
43.571
1.6%
44.21
1.6%
44.841
1.6%
45.471
1.6%
46.11
1.6%
46.751
1.6%
ValueCountFrequency (%)
69.731
1.6%
69.51
1.6%
69.271
1.6%
68.971
1.6%
68.671
1.6%
68.371
1.6%
68.071
1.6%
67.771
1.6%
67.321
1.6%
66.871
1.6%

Interactions

2022-11-06T15:34:13.536068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:03.317604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:04.403904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:05.388369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:06.494228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:07.470772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:08.549080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:09.482103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:10.454692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:11.548143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:12.472572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:13.616778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:03.460990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:04.474464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:05.483116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:06.579367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:07.572628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:08.639633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:09.565873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:10.539515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:11.626760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:12.562394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:13.697818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:03.544371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:04.564223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:05.576596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:06.661869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:07.653594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:08.721689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:09.651712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:10.738029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:11.712933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:12.640464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:13.783442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:03.637304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:04.652337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:05.655639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:06.753405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:07.740323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:08.809072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:09.741411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:10.834015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:11.802369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:12.744670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:13.874311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:03.730775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:04.742099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:05.758260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:06.850181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:07.830644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:08.892797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:09.829431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:10.928413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:11.891497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:12.941317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:13.953671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:03.815551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:04.835846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:05.845027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:06.942511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:07.922282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:08.975042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:09.920578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:11.015331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:11.973325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:13.035479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:14.032329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:03.895337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:04.922615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:05.929441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:07.025714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:08.005338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:09.052319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:10.009655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:11.098272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:12.049837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:13.114906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:14.116677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:04.064784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:05.016366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:06.022416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:07.101836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:08.092931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:09.136658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:10.098732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:11.184360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:12.134039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:13.200204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:14.202240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:04.152030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:05.110113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:06.114959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:07.205440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:08.195324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:09.227613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:10.172805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:11.271816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:12.224113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:13.283389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:14.280652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:04.232353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:05.203863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:06.315504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:07.291073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:08.275521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:09.307210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:10.255935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:11.360777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:12.307372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:13.364190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:14.365402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:04.322519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:05.300604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:06.406324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:07.383251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:08.364821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:09.393939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:10.367680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:11.460884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:12.391275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-06T15:34:13.451774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-06T15:34:17.509951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-06T15:34:17.668566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-06T15:34:17.831289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-06T15:34:17.998884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-06T15:34:18.170116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-06T15:34:14.498227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-06T15:34:14.689867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

YearCountry NameGDP (current US$)GDP per capita (current US$)GDP growth (annual %)Imports of goods and services (% of GDP)Exports of goods and services (% of GDP)Total reserves (includes gold, current US$)Inflation, consumer prices (annual %)Population, totalPopulation growth (annual %)Life expectancy at birth, total (years)
01960India3.702988e+10820.006.834.466.745366e+081.784459545792.3141.13
11961India3.923244e+10853.725.964.306.663571e+081.704563518762.3341.74
21962India4.216148e+10902.936.034.175.127918e+083.634670241932.3442.34
31963India4.842192e+101015.995.914.286.078625e+082.954779336192.3442.94
41964India5.648029e+101167.455.693.734.991451e+0813.364890593092.3343.57
51965India5.955485e+10119-2.645.213.316.008509e+089.475001143462.2644.20
61966India4.586546e+1090-0.066.674.146.096946e+0810.805109926172.1844.84
71967India5.013494e+10967.835.954.036.637641e+0813.065219870692.1545.47
81968India5.308546e+101003.394.944.047.303527e+083.245334319092.1946.10
91969India5.844800e+101086.544.033.719.277641e+08-0.585453146702.2346.75

Last rows

YearCountry NameGDP (current US$)GDP per capita (current US$)GDP growth (annual %)Imports of goods and services (% of GDP)Exports of goods and services (% of GDP)Total reserves (includes gold, current US$)Inflation, consumer prices (annual %)Population, totalPopulation growth (annual %)Life expectancy at birth, total (years)
512011India1.823050e+1214585.2431.0824.542.987395e+118.9112576211911.3766.87
522012India1.827638e+1214445.4631.2624.533.004255e+119.4812744872151.3467.32
532013India1.856722e+1214506.3928.4125.432.980925e+1110.0212911320631.3167.77
542014India2.039127e+1215747.4125.9522.973.250810e+116.6713072465091.2568.07
552015India2.103588e+1216068.0022.1119.813.533191e+114.9113228665051.1968.37
562016India2.294798e+1217338.2620.9219.163.616943e+114.9513386363401.1968.67
572017India2.651473e+1219816.8021.9518.794.126138e+113.3313541956801.1668.97
582018India2.702930e+1219976.5323.6919.933.991672e+113.9413690033061.0969.27
592019India2.831552e+1221014.0421.2718.694.634699e+113.7313831120501.0369.50
602020India2.667688e+121928-7.2519.1018.715.902274e+116.6213963871270.9669.73